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    Verification-Before-Done

    1

    It treats verification as a first-class artifact, not an afterthought.

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    Verification-Before-Done

    Verification-Before-Done

    Example session with this skill installed

    I fixed a bug where our Node/Express app sent password reset emails twice. The job was queued in both the controller and a Mongoose post-save hook, and I removed the hook. Is it actually done?

    • Read your context and instructions
    • Compiled the verification-before-done

    Verification plan for the duplicate password reset email fix. It is not done until the checks below produce evidence.

    Definition of "working"

    • One reset request produces exactly one job in the queue and one email sent
    • Other flows that relied on the post-save hook still behave as before
    • The reset link in the email still works end to end

    Checks (evidence over inference)

    #CheckEvidence to capture
    1Trigger one reset in stagingQueue shows 1 job for that user ID
    2Inspect mail provider log1 message to the test address
    3Search the codebase for other callers of the hook's functionList of call sites, or "none"
    4Save a user through a non-reset path (profile update)No reset job queued
    5Click the reset link, set a new passwordLogin with the new password succeeds
    6Add a test asserting the enqueue is called onceTest fails on the old code, passes on the new

    "The hook is gone so it must be fixed" is inference. Checks 1, 2 and 6 turn it into evidence.

    Evidence trail

    Record per check: date, environment, command or action, observed result, link to log or screenshot. Attach it to the PR.

    Next steps

    • Run checks 1 to 5 in staging and fill in the trail
    • Write the regression test from check 6
    • Mark done only when every row has evidence

    Connects securely to your tools. The creator never sees your data.

    About this skill

    'Should work now' is the most expensive sentence in software — it means the change was reasoned about, not observed, and the next person to touch this code will pay for that assumption. This skill builds the verification protocol for your specific change: the exact commands to run, the outputs to observe, the edge cases to test manually, and the signals that confirm the fix didn't introduce a regression. It treats verification as a first-class artifact, not an afterthought. Use it before you close any ticket — give it your change, your environment, and what you've already tested; it returns the complete verification checklist that lets you say 'done' and mean it.

    • Cannot execute live code to physically verify results.
    • Relies on user providing accurate environment and stack details.
    • Does not replace automated CI/CD pipelines.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Recently published to Agensi

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    Trust & safety

    Security scan

    Security scan passed 4 months ago

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    Listed4 months ago
    Updated12 days ago

    What's inside

    Frequently Asked Questions

    This skill generates a rigorous, step-by-step verification protocol consisting of specific commands and success signals that prove a code change actually works, preventing the common 'should work now' fallacy.

    This skill is platform-agnostic and designed to work with any high-intelligence AI agent capable of code execution and terminal access, including popular development agents and custom LLM workflows.

    You receive the proprietary skill configuration and integration architecture that enables your agent to transition from 'writing code' to 'validating results' using an evidence-based framework.

    Simply provide the agent with your code change, your target environment details, and the intended behavior; the skill will then output the exact verification checklist required to confirm a successful fix.

    This skill is ideal for complex bug fixes, legacy codebase updates, and high-stakes production deployments where 'manual' edge-case testing is just as critical as automated unit tests.